Establishment of an Accurate Starch Content Analysis System for Fresh Cassava Roots Using Short-Wavelength Near Infrared Spectroscopy.

Abstract

Short-wavelength near infrared spectra in the interactance mode were collected from intact cassava roots and cassava flesh, using two portable spectrometers for the spectral regions of 720-1050 and 850-1150 nm, respectively. All starch prediction models were developed using the partial least squares regression. Good prediction performance was obtained from the cassava flesh (cross-section cut root) measurement with a correlation of prediction ( r p ) of 0.917 and standard error of prediction (SEP) of 1.73%, for both spectrometers. For the intact root, the prediction models were satisfactorily accurate with r p values of 0.687 and 0.772 and SEP of 3.151 and 2.803%, respectively. Moreover, the performance measurement of all optimum models was also evaluated according to ISO 12099:2017(E). The results showed that the predicted values were not significantly different from the actual values obtained from the standard method at 95% confidence intervals. These results showed the feasibility of using portable spectrometers to predict the starch content of fresh cassava roots.

Plant phenotyping relevance

携帯型短波長NIRで生鮮キャッサバ根のデンプン含量を非破壊推定する測定システムを開発し、予測性能と標準法との一致を検証しており、植物器官の形質取得法が中心です。

Code and data availability

The article reports SWNIR starch prediction models for cassava roots but contains no data availability statement, no public deposit of spectra or starch measurements, and no author code or model release. The only software mentioned is the commercial Unscrambler v9.7, and no public URLs are present in the supplied text.

No evidence-backed public reproduction asset is currently recorded.